def setup_sectlabel_bow_glove_infer(request, clf_datasets_manager,
tmpdir_factory):
track_for_best = request.param
sample_proportion = 0.5
datasets_manager = clf_datasets_manager
word_embedder = WordEmbedder(embedding_type="glove_6B_50")
bow_encoder = BOW_Encoder(embedder=word_embedder)
classifier = SimpleClassifier(
encoder=bow_encoder,
encoding_dim=word_embedder.get_embedding_dimension(),
num_classes=2,
classification_layer_bias=True,
datasets_manager=datasets_manager,
)
train_metric = PrecisionRecallFMeasure(datasets_manager=datasets_manager)
validation_metric = PrecisionRecallFMeasure(
datasets_manager=datasets_manager)
test_metric = PrecisionRecallFMeasure(datasets_manager=datasets_manager)
optimizer = torch.optim.Adam(params=classifier.parameters())
batch_size = 1
save_dir = tmpdir_factory.mktemp("experiment_1")
num_epochs = 1
save_every = 1
log_train_metrics_every = 10
engine = Engine(
model=classifier,
datasets_manager=datasets_manager,
optimizer=optimizer,
batch_size=batch_size,
save_dir=save_dir,
num_epochs=num_epochs,
save_every=save_every,
log_train_metrics_every=log_train_metrics_every,
train_metric=train_metric,
validation_metric=validation_metric,
test_metric=test_metric,
track_for_best=track_for_best,
sample_proportion=sample_proportion,
)
engine.run()
model_filepath = pathlib.Path(save_dir).joinpath("best_model.pt")
infer = ClassificationInference(
model=classifier,
model_filepath=str(model_filepath),
datasets_manager=datasets_manager,
)
return infer
def build_sectlabel_bow_elmo_model(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
embedder = BowElmoEmbedder(layer_aggregation="last")
encoder = BOW_Encoder(aggregation_type="sum", embedder=embedder)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=1024,
num_classes=data_manager.num_labels["label"],
classification_layer_bias=True,
datasets_manager=data_manager,
)
infer_client = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return infer_client
def setup_simple_classifier():
BATCH_SIZE = 1
NUM_TOKENS = 3
EMB_DIM = 300
VOCAB_SIZE = 10
NUM_CLASSES = 3
embedding = Embedding.from_pretrained(torch.zeros([VOCAB_SIZE, EMB_DIM]))
embedder = VanillaEmbedder(embedding_dim=EMB_DIM, embedding=embedding)
labels = torch.LongTensor([[1]])
encoder = BOW_Encoder(emb_dim=EMB_DIM,
embedder=embedder,
dropout_value=0,
aggregation_type="sum")
tokens = np.random.randint(0,
VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TOKENS))
tokens = torch.LongTensor(tokens)
simple_classifier = SimpleClassifier(
encoder=encoder,
encoding_dim=EMB_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=False,
)
iter_dict = {"tokens": tokens, "label": labels}
return iter_dict, simple_classifier, BATCH_SIZE, NUM_CLASSES
def _get_model(self) -> nn.Module:
embedding_type = self.hparams.get("emb_type")
word_embedder = WordEmbedder(embedding_type=embedding_type)
elmo_embedder = ElmoEmbedder(datasets_manager=self.data_manager)
embedder = ConcatEmbedders([word_embedder, elmo_embedder])
hidden_dim = self.hparams.get("hidden_dim")
combine_strategy = self.hparams.get("combine_strategy")
bidirectional = self.hparams.get("bidirectional")
encoder = LSTM2VecEncoder(
embedder=embedder,
hidden_dim=hidden_dim,
combine_strategy=combine_strategy,
bidirectional=bidirectional,
)
classifier_encoding_dim = 2 * hidden_dim if bidirectional else hidden_dim
model = SimpleClassifier(
encoder=encoder,
encoding_dim=classifier_encoding_dim,
num_classes=3,
classification_layer_bias=True,
datasets_manager=self.data_manager,
)
return model
def build_model(self):
word_embedder = WordEmbedder(
embedding_type=self.hparams.get("embedding_type"),
device=self.hparams.get("device"))
elmo_embedder = ElmoEmbedder(device=self.hparams.get("device"))
embedder = ConcatEmbedders([word_embedder, elmo_embedder])
encoder = LSTM2VecEncoder(
embedder=embedder,
hidden_dim=self.hparams.get("hidden_dim"),
combine_strategy=self.hparams.get("combine_strategy"),
bidirectional=self.hparams.get("bidirectional"),
device=torch.device(self.hparams.get("device")),
)
classiier_encoding_dim = (2 * self.hparams.get("hidden_dim")
if self.hparams.get("bidirectional") else
self.hparams.get("hidden_dim"))
model = SimpleClassifier(
encoder=encoder,
encoding_dim=classiier_encoding_dim,
num_classes=self.hparams.get("num_classes"),
classification_layer_bias=True,
datasets_manager=self.data_manager,
device=self.hparams.get("device"),
)
return model
def _get_model(self):
elmo_embedder = BowElmoEmbedder(layer_aggregation="sum")
# instantiate the vanilla embedder
vanilla_embedder = WordEmbedder(
embedding_type=self.hparams.get("emb_type"))
# concat the embeddings
embedder = ConcatEmbedders([vanilla_embedder, elmo_embedder])
hidden_dim = self.hparams.get("hidden_dim")
bidirectional = self.hparams.get("bidirectional")
combine_strategy = self.hparams.get("combine_strategy")
encoder = LSTM2VecEncoder(
embedder=embedder,
hidden_dim=hidden_dim,
bidirectional=bidirectional,
combine_strategy=combine_strategy,
)
encoding_dim = (2 * hidden_dim if bidirectional
and combine_strategy == "concat" else hidden_dim)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=encoding_dim,
num_classes=23,
classification_layer_bias=True,
datasets_manager=self.data_manager,
)
return model
def build_sectlabel_elmobilstm_model(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
DEVICE = "cpu"
EMBEDDING_TYPE = "glove_6B_50"
HIDDEN_DIM = 512
BIDIRECTIONAL = True
COMBINE_STRATEGY = "concat"
elmo_embedder = BowElmoEmbedder(cuda_device_id=-1 if DEVICE ==
"cpu" else int(DEVICE.split("cuda:")[1]))
vanilla_embedder = WordEmbedder(embedding_type=EMBEDDING_TYPE)
embedders = ConcatEmbedders([vanilla_embedder, elmo_embedder])
encoder = LSTM2VecEncoder(
embedder=embedders,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
device=torch.device(DEVICE),
)
encoding_dim = (2 * HIDDEN_DIM if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIM)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=encoding_dim,
num_classes=23,
classification_layer_bias=True,
datasets_manager=data_manager,
)
inference = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return inference
def build_model(self):
embedder = WordEmbedder(embedding_type=self.hparams.get("emb_type"))
encoder = BOW_Encoder(embedder=embedder)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=self.hparams.get("encoding_dim"),
num_classes=self.hparams.get("num_classes"),
classification_layer_bias=True,
datasets_manager=self.data_manager,
)
return model
def get_bilstm_lc_infer_parsect(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
HIDDEN_DIM = config["HIDDEN_DIMENSION"]
COMBINE_STRATEGY = config["COMBINE_STRATEGY"]
BIDIRECTIONAL = config["BIDIRECTIONAL"]
VOCAB_SIZE = config["VOCAB_SIZE"]
NUM_CLASSES = config["NUM_CLASSES"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
classifier_encoding_dim = 2 * HIDDEN_DIM if BIDIRECTIONAL else HIDDEN_DIM
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIM)
embedder = VanillaEmbedder(embedding_dim=EMBEDDING_DIM,
embedding=embedding)
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIM,
embedder=embedder,
hidden_dim=HIDDEN_DIM,
combine_strategy=COMBINE_STRATEGY,
bidirectional=BIDIRECTIONAL,
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=classifier_encoding_dim,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return inference
def setup_simple_classifier(clf_dataset_manager):
datasets_manager = clf_dataset_manager
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = BOW_Encoder(embedder=embedder)
classifier = SimpleClassifier(
encoder=encoder,
encoding_dim=50,
num_classes=2,
datasets_manager=datasets_manager,
classification_layer_bias=True,
)
train_dataset = datasets_manager.train_dataset
lines, labels = train_dataset.get_lines_labels()
return classifier, lines, labels
def build_model(self):
embedder = BowElmoEmbedder(
layer_aggregation=self.hparams.get("layer_aggregation"))
encoder = BOW_Encoder(
aggregation_type=self.hparams.get("word_aggregation"),
embedder=embedder)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=self.hparams.get("encoding_dim"),
num_classes=self.hparams.get("num_classes"),
classification_layer_bias=True,
datasets_manager=self.data_manager,
)
return model
def get_bow_bert_emb_lc_gensect_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
NUM_CLASSES = config["NUM_CLASSES"]
BERT_TYPE = config["BERT_TYPE"]
DEVICE = config["DEVICE"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedder = BertEmbedder(
emb_dim=EMBEDDING_DIM,
dropout_value=0.0,
aggregation_type="average",
bert_type=BERT_TYPE,
device=torch.device(DEVICE),
)
encoder = BOW_Encoder(
embedder=embedder, emb_dim=EMBEDDING_DIM, aggregation_type="average"
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMBEDDING_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = GenericSectDataset(**test_dataset_args)
parsect_inference = ClassificationInference(
model=model, model_filepath=model_filepath, dataset=dataset
)
return parsect_inference
def build_sectlabel_bilstm_model(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
HIDDEN_DIM = 512
BIDIRECTIONAL = True
COMBINE_STRATEGY = "concat"
classifier_encoding_dim = 2 * HIDDEN_DIM if BIDIRECTIONAL else HIDDEN_DIM
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = LSTM2VecEncoder(
embedder=embedder,
hidden_dim=HIDDEN_DIM,
combine_strategy=COMBINE_STRATEGY,
bidirectional=BIDIRECTIONAL,
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=classifier_encoding_dim,
num_classes=23,
classification_layer_bias=True,
datasets_manager=data_manager,
)
inference = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints", "best_model.pt")),
datasets_manager=data_manager,
)
return inference
def get_bow_lc_parsect_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIMENSION = config["EMBEDDING_DIMENSION"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
VOCAB_SIZE = config["VOCAB_SIZE"]
NUM_CLASSES = config["NUM_CLASSES"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIMENSION)
embedder = VanillaEmbedder(embedding_dim=EMBEDDING_DIMENSION,
embedding=embedding)
encoder = BOW_Encoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
dropout_value=0.0,
aggregation_type="sum",
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMBEDDING_DIMENSION,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
dataset.print_stats()
parsect_inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return parsect_inference
def build_sectlabel_bow_model(dirname: str):
"""
Parameters
----------
dirname : The directory where sciwing stores your outputs for the model
Returns
-------
"""
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = BOW_Encoder(embedder=embedder)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=embedder.get_embedding_dimension(),
num_classes=data_manager.num_labels["label"],
classification_layer_bias=True,
datasets_manager=data_manager,
)
infer = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return infer
def get_elmo_emb_lc_infer_gensect(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
NUM_CLASSES = config["NUM_CLASSES"]
EMBEDDING_DIMENSION = config["EMBEDDING_DIMENSION"]
LAYER_AGGREGATION = config["LAYER_AGGREGATION"]
WORD_AGGREGATION = config["WORD_AGGREGATION"]
embedder = BowElmoEmbedder(emb_dim=EMBEDDING_DIMENSION,
layer_aggregation=LAYER_AGGREGATION)
encoder = BOW_Encoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
aggregation_type=WORD_AGGREGATION,
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMBEDDING_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
dataset = GenericSectDataset(**test_dataset_args)
parsect_inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return parsect_inference
def build_model(self):
embedder = WordEmbedder(
embedding_type=self.hparams.get("embedding_type"))
encoder = LSTM2VecEncoder(
embedder=embedder,
hidden_dim=self.hparams.get("hidden_dim"),
combine_strategy=self.hparams.get("combine_strategy"),
bidirectional=self.hparams.get("bidirectional"),
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=2 * self.hparams.get("hidden_dim"),
num_classes=self.hparams.get("num_classes"),
classification_layer_bias=True,
datasets_manager=self.data_manager,
)
return model
def build_sectlabel_bow_bert(dirname: str):
exp_dirpath = pathlib.Path(dirname)
DATA_PATH = pathlib.Path(DATA_DIR)
train_file = DATA_PATH.joinpath("sectLabel.train")
dev_file = DATA_PATH.joinpath("sectLabel.dev")
test_file = DATA_PATH.joinpath("sectLabel.test")
data_manager = TextClassificationDatasetManager(
train_filename=str(train_file),
dev_filename=str(dev_file),
test_filename=str(test_file),
)
embedder = BertEmbedder(
dropout_value=0.0,
aggregation_type="average",
bert_type="bert-base-uncased",
device=torch.device("cpu"),
)
encoder = BOW_Encoder(embedder=embedder, aggregation_type="average")
model = SimpleClassifier(
encoder=encoder,
encoding_dim=768,
num_classes=23,
classification_layer_bias=True,
datasets_manager=data_manager,
)
parsect_inference = ClassificationInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints",
"best_model.pt")),
datasets_manager=data_manager,
)
return parsect_inference
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIMENSION + 1024,
embedder=embedder,
hidden_dim=HIDDEN_DIMENSION,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
device=torch.device(DEVICE),
)
encoding_dim = (2 * HIDDEN_DIMENSION if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIMENSION)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=encoding_dim,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
optimizer = optim.Adam(params=model.parameters(), lr=LEARNING_RATE)
metric = PrecisionRecallFMeasure(
idx2labelname_mapping=train_dataset.idx2classname)
engine = Engine(
model=model,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
test_dataset=test_dataset,
optimizer=optimizer,
batch_size=BATCH_SIZE,
save_dir=MODEL_SAVE_DIR,
dev_filename=str(dev_file),
test_filename=str(test_file),
)
# BowElmoEmbedder embeds sentences using ELMO
embedder = BowElmoEmbedder(layer_aggregation=args.layer_aggregation,
device=args.device)
encoder = BOW_Encoder(embedder=embedder,
aggregation_type=args.word_aggregation,
device=args.device)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=1024,
num_classes=data_manager.num_labels["label"],
classification_layer_bias=True,
datasets_manager=data_manager,
device=args.device,
)
optimizer = optim.Adam(params=model.parameters(), lr=args.lr)
train_metric = PrecisionRecallFMeasure(datasets_manager=data_manager)
dev_metric = PrecisionRecallFMeasure(datasets_manager=data_manager)
test_metric = PrecisionRecallFMeasure(datasets_manager=data_manager)
engine = Engine(
model=model,
datasets_manager=data_manager,
optimizer=optimizer,
batch_size=args.bs,
save_dir=args.model_save_dir,
def setup_engine_test_with_simple_classifier(request, tmpdir_factory):
MAX_NUM_WORDS = 1000
MAX_LENGTH = 50
vocab_store_location = tmpdir_factory.mktemp("tempdir").join("vocab.json")
DEBUG = True
BATCH_SIZE = 1
NUM_TOKENS = 3
EMB_DIM = 300
train_dataset = SectLabelDataset(
filename=SECT_LABEL_FILE,
dataset_type="train",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
word_vocab_store_location=vocab_store_location,
debug=DEBUG,
word_embedding_type="random",
word_embedding_dimension=EMB_DIM,
)
validation_dataset = SectLabelDataset(
filename=SECT_LABEL_FILE,
dataset_type="valid",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
word_vocab_store_location=vocab_store_location,
debug=DEBUG,
word_embedding_type="random",
word_embedding_dimension=EMB_DIM,
)
test_dataset = SectLabelDataset(
filename=SECT_LABEL_FILE,
dataset_type="test",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
word_vocab_store_location=vocab_store_location,
debug=DEBUG,
word_embedding_type="random",
word_embedding_dimension=EMB_DIM,
)
VOCAB_SIZE = MAX_NUM_WORDS + len(train_dataset.word_vocab.special_vocab)
NUM_CLASSES = train_dataset.get_num_classes()
NUM_EPOCHS = 1
embedding = Embedding.from_pretrained(torch.zeros([VOCAB_SIZE, EMB_DIM]))
labels = torch.LongTensor([1])
metric = PrecisionRecallFMeasure(
idx2labelname_mapping=train_dataset.idx2classname)
embedder = VanillaEmbedder(embedding_dim=EMB_DIM, embedding=embedding)
encoder = BOW_Encoder(emb_dim=EMB_DIM,
embedder=embedder,
dropout_value=0,
aggregation_type="sum")
tokens = np.random.randint(0,
VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TOKENS))
tokens = torch.LongTensor(tokens)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMB_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=False,
)
optimizer = optim.SGD(model.parameters(), lr=0.01)
engine = Engine(
model,
train_dataset,
validation_dataset,
test_dataset,
optimizer=optimizer,
batch_size=BATCH_SIZE,
save_dir=tmpdir_factory.mktemp("model_save"),
num_epochs=NUM_EPOCHS,
save_every=1,
log_train_metrics_every=10,
metric=metric,
track_for_best=request.param,
)
options = {
"MAX_NUM_WORDS": MAX_NUM_WORDS,
"MAX_LENGTH": MAX_LENGTH,
"BATCH_SIZE": BATCH_SIZE,
"NUM_TOKENS": NUM_TOKENS,
"EMB_DIM": EMB_DIM,
"VOCAB_SIZE": VOCAB_SIZE,
"NUM_CLASSES": NUM_CLASSES,
"NUM_EPOCHS": NUM_EPOCHS,
}
return engine, tokens, labels, options
def get_elmo_bilstm_lc_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
DEVICE = config["DEVICE"]
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
VOCAB_SIZE = config["VOCAB_SIZE"]
HIDDEN_DIM = config["HIDDEN_DIMENSION"]
BIDIRECTIONAL = config["BIDIRECTIONAL"]
COMBINE_STRATEGY = config["COMBINE_STRATEGY"]
NUM_CLASSES = config["NUM_CLASSES"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIM)
elmo_embedder = BowElmoEmbedder(
layer_aggregation="sum",
cuda_device_id=-1 if DEVICE == "cpu" else int(
DEVICE.split("cuda:")[1]),
)
vanilla_embedder = VanillaEmbedder(embedding=embedding,
embedding_dim=EMBEDDING_DIM)
embedders = ConcatEmbedders([vanilla_embedder, elmo_embedder])
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIM + 1024,
embedder=embedders,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
device=torch.device(DEVICE),
)
encoding_dim = (2 * HIDDEN_DIM if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIM)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=encoding_dim,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return inference
dev_filename = DATA_DIR.joinpath("genericSect.dev")
test_filename = DATA_DIR.joinpath("genericSect.test")
data_manager = TextClassificationDatasetManager(
train_filename=train_filename,
dev_filename=dev_filename,
test_filename=test_filename,
)
embedder = WordEmbedder(embedding_type=args.emb_type)
encoder = BOW_Encoder(embedder=embedder)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=50,
num_classes=12,
classification_layer_bias=True,
datasets_manager=data_manager,
)
optimizer = optim.Adam(params=model.parameters(), lr=args.lr)
train_metric = PrecisionRecallFMeasure(datasets_manager=data_manager)
dev_metric = PrecisionRecallFMeasure(datasets_manager=data_manager)
test_metric = PrecisionRecallFMeasure(datasets_manager=data_manager)
engine = Engine(
datasets_manager=data_manager,
model=model,
optimizer=optimizer,
batch_size=args.bs,
save_dir=args.model_save_dir,